Anomaly Detection

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40 papers in the last four weeks, up 33% on the four weeks before. 0.4% of all new papers.

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Causal Anomaly Detection for Lithium-Ion Battery Degradation
May 17, 2026Dieter W. Heermann, Hagen HeermannPredictive Maintenance Time-Series Anomaly Detection
Institute for Theoretical Physics, Heidelberg University, Philosophenweg 19, D-69120 Heidelberg, Germany · Intilion GmbH, Wollmarktstraße 115c, D-33098 Paderborn, Germany
An Efficient Machine Learning-based Framework for Detection and Prevention of Frauds in Telecom Networks
May 17, 2026Praveen Hegde, Mishal ShahAnomaly Detection
Associate Director-Emerging Commercial Platforms Verizon Atlanta, USA · Senior Software Engineer Bloomberg LP Jersey City, USA
Toward Near-Real-Time Marine Oil Spill Detection in SAR Imagery using Quantum-Assisted SVM
May 17, 2026Joseph Strauss, Jyotsna SharmaRemote Sensing Image Segmentation Quantum Annealing
Division of Computer Science & Engineering Louisiana State University Baton Rouge, United States · Department of Petroleum Engineeering Louisiana State University Baton Rouge, United States
Filter-then-Verify: A Multiphase GNN and ModernBERT Framework for Social Engineering Detection in Email Networks
May 17, 2026Barsat Khadka, Prasant Koirala, Kshitiz Neupane +1Anomaly Detection
School of Computing Sciences and Computer Engineering, The University of Southern Mississippi, 118 College Drive, Hattiesburg, MS 39406, USA
Privacy-Preserving Generation Fraud Detection for Distributed Photovoltaic Systems: A Solar Irradiance-Fused Federated Learning Framework
May 16, 2026Xiaolu Chen, Chenghao Huang, Yanru Zhang +1Privacy-Preserving ML Federated Learning
School of Computer Science and Technology, University of Electronic Science and Technology of China, Chengdu, China · Department of Data Science and AI, Faculty of IT, Monash University, Melbourne, VIC 3800, Australia · Department of Data Science and AI, Faculty of IT and Monash Energy Institute, Monash University, Melbourne, VIC 3800, Australia +1
Res $^2$ CLIP: Few-Shot Generalist Anomaly Detection with Residual-to-Residual Alignment
May 15, 2026Xinyue Liu, Jianyuan Wang, Biao Leng +1Few-Shot Anomaly Detection VLM Adaptation
Beihang University · University of Science and Technology Beijing · Beijing Jiaotong University
Ti-iLSTM: A TinyDL Approach for Logic-Level Anomaly Detection in Industrial Water Treatment Systems
May 15, 2026Mandar Joshi, Farzana Zahid, Judy Bowen +3Efficient Neural Network Inference Industrial Anomaly Detection
University of Waikato, Hamilton, New Zealand · Auckland University of Technology, Auckland, New Zealand · Aalto University, Espoo, Finland
From Data to Action: Accelerating Refinery Optimization with AI
May 14, 2026Dániel Pfeifer, Ábrahám Papp, Tibor Bernáth +4Industrial Anomaly Detection Unsupervised Anomaly Detection
aBudapest University of Technology and Economics, Műegyetem rkp. 3., Budapest, 1111, Hungary · bMOL Group, Dombóvári út 28., Budapest, 1117, Hungary
Logical Grammar Induction via Graph Kolmogorov Complexity: A Neuro-Symbolic Framework for Self-Healing Clinical Data Integrity
May 14, 2026Abolfazl Zarghani, Amir MalekesfandiariHealthcare Neuro-Symbolic Reasoning
Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
Product-Aware Deep Autoencoders for Robust Process Monitoring in Multi-Product Cyber-Physical Systems
May 13, 2026MD Shafikul Islam, Jordan CardenCyber-Physical Systems Autoencoders
Uncertainty-Driven Anomaly Detection for Psychotic Relapse Using Smartwatches: Forecasting and Multi-Task Learning Fusion
May 13, 2026Nikolaos Tsalkitzis, Panagiotis P. Filntisis, Petros Maragos +1Wearable Health Monitoring Time-Series Anomaly Detection
School of ECE, National Technical University of Athens, Greece · Robotics Institute, Athena Research and Innovation Center, Greece · HERON - Hellenic Robotics Center of Excellence, Athens, Greece
Weakly-Supervised Spatiotemporal Anomaly Detection
May 13, 2026Urvi Gianchandani, Praveen Tirupattur, Mubarak ShahAnomaly Localization Weakly Supervised Video Anomaly Detection
University of Texas at Dallas · University of Central Florida
Conformal Anomaly Detection in Python: Moving Beyond Heuristic Thresholds with 'nonconform'
May 13, 2026Oliver Hennhöfer, Maximilian Kirsch, Christine PreisachFDR Control Conformal Prediction
Intelligent Systems Research Group, Karlsruhe University of Applied Sciences
Learning Feature Encoder with Synthetic Anomalies for Weakly Supervised Graph Anomaly Detection
May 12, 2026Yingjie Zhou, Yuqin Xie, Fanxing Liu +3Graph Representation Learning Anomaly Detection
Sichuan University, Chengdu 610065, China · University of Connecticut, Storrs, CT 06269, USA · University of Electronic Science and Technology of China, Chengdu 611731, China +1
Conditional anomaly detection methods for patient-management alert systems
May 11, 2026Michal Valko, Gregory Cooper, Amy Seybert +3Clinical Decision Support Anomaly Detection
University of Pittsburgh, PA
Hypergraph-Enhanced Training-Free and Language-Free Few-Shot Anomaly Detection
May 11, 2026Guohuan Xie, Xin He, Dingying Fan +2Few-Shot Anomaly Detection Medical Image Anomaly Detection
Nankai University · Tianjin University of Technology · Tsinghua University
AnomalyClaw: A Universal Visual Anomaly Detection Agent via Tool-Grounded Refutation
May 11, 2026Xi Jiang, Yinjie Zhao, Zesheng Yang +1Tool-Using Vision-Language Agents VLM Reasoning
Department of Computer Science and Engineering, Southern University of Science and Technology (SUSTech), Shenzhen, China · School of EEE, Nanyang Technological University (NTU), Singapore · CFAR, Agency for Science, Technology and Research (A*STAR), Singapore
When Normality Shifts: Risk-Aware Test-Time Adaptation for Unsupervised Tabular Anomaly Detection
May 11, 2026Wei Huang, Hezhe Qiao, Kailai Zhang +3Test-Time Adaptation Tabular Anomaly Detection
Multi-Tier Labeling and Physics-Informed Learning for Orbital Anomaly Detection at Scale
May 10, 2026Yong FuWeakly Supervised Learning Physics-Informed ML
Substratum Labs, Inc.
FragBench: Cross-Session Attacks Hidden in Benign-Looking Fragments
May 10, 2026Astha Mehta, Niruthiha Selvanayagam, Cedric Lam +10LLM Safety Benchmarks Language Model Safety Evaluation
Isabella · ÉTS Montreal / SPAR · Mila / Lida Safety
FedCIGAR: A Personalized Reconstruction Approach for Federated Graph-level Anomaly Detection
May 10, 2026Yunfeng Zhao, Yixin Liu, Qingfeng Chen +3Federated Graph Learning Reconstruction-Based Anomaly Detection
Guangxi University · Griffith University
Detect, Localize, and Explain: Interactive Hierarchical Log Anomaly Analytics with LLM Augmentation
May 9, 2026Lei Ma, Suhani Chaudhary, Ethan Shanbaum +5Online Anomaly Detection Interpretable Anomaly Detection
Worcester Polytechnic Institute, USA · UC Riverside, USA · University of Nevada, Las Vegas, USA
The Payment Heterogeneity Index: An Integrated Unsupervised Framework for High-Volume Procurement Oversight and Decision Support
May 9, 2026Kyriakos ChristodoulidesUnsupervised Anomaly Detection Anomaly Detection
Philips University, Department of Computer Science, Nicosia, Cyprus · Novel Intelligence, London, UK
ORACLE: Anticipating Scams from Partial Trajectories in Streaming App Usage
May 9, 2026Wenbo Gao, Songbai Tan, Zhongan Wang +6Online Anomaly Detection Financial Fraud Detection
Harbin Institute of Technology, Shenzhen · Shenzhen University · Zhejiang University +4
Outlier detection for patient monitoring and alerting
May 9, 2026Miloš Hauskrecht, Iyad Batal, Michal Valko +3Healthcare Clinical Decision Support
Department of Computer Science, University of Pittsburgh, Pittsburgh, PA, USA · The Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, USA · Inria Lille-Nord Europe, equipe SequeL, Lille, France +2
Seeing the Needle in the Haystack: Towards Weakly-Supervised Log Instance Anomaly Localization via Counterfactual Perturbation
May 9, 2026Yutszyuk Wong, Wentai Wu, Yuen-Ying Yeung +1Online Anomaly Detection Anomaly Localization
Jinan University · Guangzhou, China · South China University of Technology
Contour-Native Bridge Defect Detection and Compact Digital Archiving with Frequency-Supervised Fourier Contours
May 9, 2026Jin Liu, Wang Wang, Hongxu Pu +4Frequency-Domain Image Processing Structural Health Monitoring
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 430072, China · Sustainability X-Lab, The University of Hong Kong, Hong Kong, China · Department of Cyber Security, Southeast University, No. 2, Southeast University Road, Nanjing, 211102, China +2
Simultaneous Monitoring of Shape and Surface Color via 4D Point Clouds: A Registration-free Approach
May 9, 2026Mariafrancesca Patalano, Giovanna Capizzi, Kamran PaynabarPoint Cloud Anomaly Detection Anomaly Localization
Department of Statistical Sciences, University of Padua · School of Industrial and Systems Engineering, Georgia Institute of Technology
GRASP -- Graph-Based Anomaly Detection Through Self-Supervised Classification
May 8, 2026Robin Buchta, Carsten Kleiner, Felix Heine +1Cybersecurity Anomaly Detection
A Task-Agnostic Algebraic Integrity Metric for Event-Camera Streams Toward SOTIF-Compliant Perception using Pearson Correlation Coefficient
May 8, 2026Arthur de Miranda NetoEvent-Based Vision Autonomous Driving Perception
Federal University of Lavras (UFLA), Brazil

May 17, 2026, cond-mat.mtrl-sciJ/K move · Enter open · S save

Causal Anomaly Detection for Lithium-Ion Battery Degradation

Dieter W. Heermann, Hagen Heermann

Institute for Theoretical Physics, Heidelberg University, Philosophenweg 19, D-69120 Heidelberg, Germany · Intilion GmbH, Wollmarktstraße 115c, D-33098 Paderborn, Germany

Predictive Maintenance Time-Series Anomaly Detection Lithium-Ion Batteries Anomaly Detection

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Reliable early detection of lithium-ion battery degradation requires health indicators that are physically interpretable and computable from routine cycler telemetry without access to the degradation region. We introduce \textsc{CausalHealth}, a framework that applies causal graph discovery and

k

-nearest-neighbour transfer entropy to per-cycle voltage, current, temperature, and resistance time series, and organises twelve resulting anomaly scores into three signal-class bundles (Magnitude-shift, Predictive-residual, Complexity-entropy) -- with Isolation Forest reported separately as it falls below the bundle reliability threshold -- to characterise detection sensitivity across ten commissioning fractions (5--30,%). The Magnitude-shift class achieves 100,% detection across all seven tested cells spanning LFP (MIT--Stanford MATR) and LCO (NASA PCoE, CALCE CS2) chemistries, with a lead time of up to 402 cycles before conventional capacity-threshold failure on gradual-fade cells. A Reliability-Weighted Master Health Index (RWMHI) -- a cross-bundle fusion of five high-reliability detectors weighted by inverse coefficient of variation -- improves lead time by 15--52 cycles over the class median on long-lived cells while maintaining 100,% detection. Validation against electrochemical impedance spectroscopy on an NMC prismatic cell provides independent physical grounding: transfer entropy

\mathrm{TE}(R \!\to\! V)

correlates with charge-transfer resistance

R_{\mathrm{ct}}

(pooled

r = +0.990

; temperature-controlled partial

r = +0.898

), and an Arrhenius analysis of both quantities yields an activation energy consistent with published NMC charge-transfer kinetics. These results are evaluated on seven cells across three benchmark datasets.

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